Method and apparatus for identifying MICR characters

ABSTRACT

A method and apparatus for reading MICR characters is disclosed, in which both magnetic and optical data may be read and stored. Each type of data is analyzed to identify possible character values. In one disclosed analysis method, the data is first evaluated in a coarse analysis then analyzed using a fine analysis which may lead to the identification of characters. The analysis of characters may involve generating frequency spectrum data from scanned data and assigning character values based on the frequency spectrum data.

BACKGROUND OF THE INVENTION

The present invention relates to optical and magnetic characterrecognition of a type which may be used to read standard MICRinformation on financial instruments such as bank checks.

Bank checks are an important part of a modern financial system. Eachyear in the United States more than 50 billion checks are processed forpayment. To faciliate check processing a set of standards has beencreated for certain information, such as financial institution andaccount number, which is printed on blank checks in standard locations.This set of standards, referred to generally as MICR defines thecharacters and their placement on checks. The MICR characters arewritten in magnetic ink and, as a result have both an optical image anda magnetic image.

In order to process the large number of bank checks required each day,automated readers have been employed which identify, as well as theycan, the information conveyed by the MICR characters on a check. Inreality the readers, which move the checks across a reading head, maynot be able to read all MICR data due for example, to printing quality,foreign marks in the MICR region of the check or attempts to improperlymodify the MICR information. Improvements are needed in methods andapparatus for automatically interpreting MICR characters.

This need is met and a technical advance is achieved in accordance withthe present invention which uses both magnetic and optical reading ofthe MICR characters and identifies characters so read by analyzing datarepresenting both optical and magnetic readings to select which of theMICR characters have been read.

As described herein a check is fed through reading apparatus whichidentifies the location of the MICR characters and which bothmagnetically and optically reads the identified character locations. Thedata for each type or reading is normalized to values suitable forcombination of the two types of data which are then merged to form acombined magnetic and optical representation of the scanned characters.The combined scanned data is analyzed by comparison with a combinedmagnetic and optical representation of a standard character templateset. The result of the analysis results in the assignments of charactervalues to the scanned character data.

The combined scanned data and the combined template data may be in theform of vectors which are analyzed or compared using vector analysistools such as multiplication, error value determination and eigenvectordecomposition

The analysis of combined magnetic and optical data may be used alone toidentify scanned character values or it may be used in conjunction withseparate analysis of the scanned optical data and scanned magnetic data.When used with separate data analysis the combined data analysisdiscussed herein may be used for all scanned characters or foridentifying characters where the separate analysis did not sufficientlyresolve a character.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a representation of a bank check having MICR characters;

FIG. 2 is a block diagram of check scanning apparatus;

FIG. 3 represents the output of a magnetic scanning element; and

FIG. 4 is a flow diagram of a first character recognition process; and

FIG. 5 is a flow diagram of a second character recognition process.

DESCRIPTION

FIG. 1 illustrates a common bank check 19 having imprinted thereon aseries of MICR characters 20 which identify significant information tothe banking community such as the bank identity and the instrumentnumber. The MICR characters 20 are visually readable and are printedusing a magnetic or magnetizable ink. The MICR characters and theirplacement on the check are standardized and, depending primarily onprinting accuracy, can be read using a magnetic reading device which maycomprise a plurality of read heads spaced across the standard zone inwhich the MICR characters are expected. The characters are also visuallyreadable and thus can be scanned by electronic optical scanningapparatus.

FIG. 2 is a schematic block diagram representing an MICR characterscanning device 21 for checks. During the scanning operation a check ismoved in the direction of arrow 23 across magnetic and optical readers.The motors and rotating check driving apparatus to move the checks aregenerally known in the art. One advantageous apparatus for moving checkspast readers is disclosed in U.S. application Ser. No. 11/199,685, filedAug. 9, 2005, which is incorporated herein by reference. The speed ofcheck motion is controlled and synchronized with the MICR reading by acontroller 25 so that the characters as read accurately represent theMICR characters as printed. Scanning device 21 includes controller 25which controls the scanning device 21 to accurately read the check.Controller 25 may include one or more microprocessors (not shown) and amemory 27 for storing a program for the microprocessors and the datacollected as well as data representing the expected characters. Datarepresenting the expected characters is referred to herein as MICRcharacter templates. An optical reader 29 and a magnetic reader 31 areused to “read” the MICR characters on the check. Magnetic reader 31 iscoupled to controller 25 by means of a magnetic character interface 35.Similarly, optical reader 29 is coupled to controller 25 by an opticalcharacter interface 37. Also under the control of the controller 25 is apre-magnetizer 33 which boosts the magnetic dipole strength in themagnetic ink before the ink is read by magnetic reader 31. As a check ismoved through the reading device 21 each of the MICR characters is firstmagnetized, then read by the optical reader 29 and finally by themagnetic reader 31.

The optical character reader 29 comprises a linear array of opticalsensing elements spaced such that 300 dots per inch can be sampled. Theoptical sensing elements are read by the optical interface 37 incoordination with the movement of the check. The rate of reading thesensing elements is controlled to provide a 300 dot per inch (dpi) scanof the entire check. The light intensities of the sensed dots are eachconverted into digital pixel form by the optical interface 37 and thefull check image is stored by the controller 25. The full image may beprovided to banking institutions who use such check images for theircustomer data bases.

After storage, the collected check image is analyzed to identify theMICR band 20 which should be found at the bottom of the check face in ornearly in the position dictated by the MICR standard. When discovered,each character of the MICR character string will be in a characterposition approximately 35 dots high and 27 dots long. The beginning offirst character position, as well as later characters in the string maybe identified by the change of scanned optical pixels from gray toblack. After each character position is identified, the image in thatcharacter position is compared to each template of the set of opticalcharacter templates stored in memory 27. Each comparison with acharacter template may comprise matrix multiplication in which the pixelvalue at a given point of the detected optical image is multiplied bythe character value at the same (or nearly the same) point of thetemplate. The sum of all of the multiplications for an image in acharacter position and one of the template is accumulated and used as anindicator of a match with the compared template. This matrixmultiplication value is then compared to an image threshold value andthe particular template character and its multiplication value are savedas a possible match when the multiplication value exceeds the imagethreshold value.

The above analysis discusses a single comparison between the image in acharacter position and each of a plurality of character templates.Advantages may be achieved when shifted comparisons are performedbetween the image in a character position and each character template.The sampled optical values of the image in a character position may beconsidered to form a rectangular array 35 pixels high by 27 pixels wideas can the character template. When the two compared arrays are exactlyin register a maximum value for the matrix multiplication will beachieved, however such exact registration may not exist with sampledimage. Accordingly, the sampled image may be advantageously shifted by apredetermined number of pixels in the vertical direction and a newcomparison performed. Such image shifting and comparison may also beperformed in the horizontal direction. The largest numerical value forthe matrix multiplication after a predetermined number of such spaciallyadjusted comparisons is then selected as the representation of the imagein the character position. After the predetermined number of iterativecomparisons has been performed, the largest value and the templatecharacter with which the largest value was achieved are compared to apredetermined image value threshold and the character value is ignoredwhen the image value is less than the threshold.

After the comparison of the image in a character position with alltemplate characters has been completed, an analysis occurs to identifythe character and a confidence level for the identified character. Atthis point of the analysis no character value, one character value ormultiple character values may be stored in association with eachcharacter position. When no matrix multiplication value has achieved theimage value threshold, no character is recorded for the characterposition being analyzed. When one character has been identified, itsassociated image value is analyzed against predetermined confidencethresholds and a confidence parameter is assigned to indicate confidencein the selected character. The confidence level may be substantiallycontinuous from no-confidence to confidence or it may be in rangesindicating confidence, no-confidence or ranges in between. When morethan one character has scored above the threshold value, each isrecorded as a possible matching character and each is associated with ano-confidence level as well as a value indicating “nearest neighbor”.

The magnetic reader, as previously discussed, consists of a linear arrayof magnetic read heads. Because no magnetic data is expected on thecheck except for the MICR character string, the reading heads aredisposed to read in the standardized MICR character zone. As the checkis moved past magnetic reader 31 traces, such as shown in FIG. 3, aregenerated and sent to magnetic interface 37 which digitizes the signalsand applies the digital representations to controller 25 where they arestored. After the magnetic character data is stored it is analyzed toidentify the character positions in the MICR character string and tothen analyze the data in the character positions to identify which ofthe plurality of characters is represented.

The magnetic character recognition begins by identifying the peaks ofthe magnetic signal to identify the beginning of the character position.Each peak in the incoming data string is identified and when the peakexceeds a predetermined threshold (see line 41 of FIG. 3) its locationand value are recorded. In addition to comparing the detected peaks tothe peak threshold, false peaks such as shown at 43 are excluded fromthe data. These false peaks are detected by requiring a down slope aftera peak to have a predetermined down slope length. After the peaks areidentified they are set to a normalizing peak value and the magneticdata below/between the peaks is set to a normalizing valley value. In apreferred embodiment, the normalizing peak and valley values are thepeak and valley values of the character templates. In some embodiments,the data may also undergo phase normalization by shifting the peaks tohave spacing expected from the various character templates.

After normalizing the value and position of the detected peaks,character matching begins to identify a character for each detectedcharacter position. Magnetic character recognition is performed bycomparing the stored data representing magnetic peaks with stored MICRcharacter templates. The comparison comprises matrix multiplication ofthe peaks in the character position with the values of the template in amanner similar to optical recognition. As with optical recognition, thepeaks of the signal in a character position are compared with all of thetemplates and with regard to any character template, the characterposition data may be shifted to achieve the maximum value of the matrixmultiplication result. Further, it has been found that improved resultscan be achieved if the location of the individual peaks is varied withrespect to the other peaks within a limited range. After the maximumvalue is found for a character position and character template the valueis compared to a threshold and when the threshold is exceeded thecharacter is stored along with its multiplication value. After allcharacter templates have been compared to the data in the characterposition, a character is assigned to the character position along withan indication of confidence.

When only one character is identified, a confidence value determinedfrom the matrix multiplication is stored in association to indicatehigh, low or intermediate confidence. In those cases where twocharacters are identified above the magnetic character threshold theyare recorded along with an indication of “nearest neighbor” (lowconfidence).

It has been determined that improvement may be achieved if each of theoptical and magnetic character recognition operations discussed above isundertaken in two stages. The first stage, called the coarse stage, isperformed as above described. The second stage, called the strict stage,is undertaken after the coarse stage and is performed using some of thecharacteristics, such as character position, identified during thecoarse stage.

In the coarse stage, the pattern matching determines if the recognizedcharacter has a high (above a pre-set threshold) or a low (below thepre-set threshold) level of confidence. This works for most cases, butit occasionally results in recognizing the wrong character, i.e., thealgorithm thinks the character is a 5 instead of a 2, defeating the“nearest neighbor” uncertainty check. There are characters that aresimilar and setting the uncertainty threshold too high results in notrecognizing characters. “Too low” and you recognize the wrong character,“too high” and you do not recognize at all. In the strict stage, asecond round of stricter matching score calculation is applied topreviously recognized characters to improve character recognition.

Both the reference templates and the detected values consist of positivepeaks and negative peaks with flat zones between the peaks (FIG. 3). Theflat zones of the template characters have a value of zero and flatzones of the detected characters should also have a value of zero. Underthe rules of the coarse evaluation stage a zero is added to the matrixmultiplication value when either the template or the detected characterhas a value of zero when the other does not. Under the rules of thestrict stage evaluation, when one character, either the templatecharacter or the detected character, has a value of zero, the value ofthe other character is subtracted from the matric multiplication value.Such subtraction constitutes a penalty when one signal is zero and theother is not.

Signal peaks or valleys of the collected character can have values thatare actually higher than the corresponding templates. It is true thecollected characters are normalized, but using the reference of thecharacter itself instead of a universal reference. In other words thenormalization value differs from character to character (some checkshave stronger ink than others). As a result, if the collectedcharacter's peaks have higher values (strong ink in that check) than thetemplates the score will be artificially higher (a higher peak valuedoes not define a character better because a character is defined by thepresence or absence of a peak). In the stricter score calculation rule,the peak value is clipped at the value of the template. For example, ifthe template value is 50 and the collected data value is 100, the scorewould have been 50*100=5000 under the coarse evaluation rules, but underthe stricter rules it will be only 50*50=2500. This greatly reduces thechances of recognizing the wrong character. Characters are selected bycomparing the matrix multiplication values determined by the stricterrules to a predetermined threshold and selection of the character withthe highest value.

In the preceding embodiments the peaks and valleys of the magneticallydetected characters are compared to peaks and valleys of the charactertemplates. This method works well when registration of the characters ofthe detected signal and the template are aligned. There are times whenalignment may not be correct because the starting pulse used to detect acharacter is distorted or a false pulse is present. In these cases,registration will not be correct and character recognition may fail.Evaluating detected magnetic characters in the frequency domain in placeof or in conjunction with peak positional evaluation provides a morecharacter position insensitive method for analyzing characters. In thismethod, the character recognition is done by matching a frequency domainrepresentation of the magnetically detected character with the templatein the frequency domain representation of the magnetic signal. Prior tocheck scanning the standard MICR characters are analyzed and a frequencydomain representation of each character is prepared. When scanning orfrequency spectrum beginning characters are extracted from the magneticsignal by detecting the approximate location of the starting point of aMICR character and extracting a subset of data that contains the entirecharacter. The starting index of this data set is expanded by N samplesto guarantee that the entire character includes all of the startingpulses. The time domain waveform of the area that contains the characteris then converted to a frequency representation by performing a fastfourier transform (FFT) or any other method of transforming to thefrequency domain. This creates a frequency spectrum of the detectedcharacter. The magnitude of the individual frequencies of the spectrumis then calculated by the square root of the square of the real andimaginary components. (M=sqrt(x̂2=ŷ2). Finally, the detected frequencyspectrum is compared against the frequency spectrums of a set ofcharacter templates to find a best character match.

Although optional, it is suggested that before the frequency domaintemplate matching is performed the signal power in the frequency domainis normalized. Variations in inks and hardware can cause the signalamplitude of the magnetic characters to vary. To remove that variablefrom the recognition algorithm, the power in the character waveform isalways normalized to 1 before a template match is performed. Also, ithas been found that the removal of high frequency components of thespectrum before matching with the template may improve recognitionresults. Removal of higher frequencies is desirable because the detectedmagnetic waveform often has spurious signal components that are not fromthe character to be recognized, such as signal dropouts, noise, suddenacceleration/deceleration and other such problems. These sources oferrors are usually faster transients than the expected signal and can beremoved by discarding the high frequencies in the frequency domainsignal.

While the frequency domain matching is relative insensitive to preciselydetecting the character starting point, missing it by a large number ofsamples may create errors, the locating of the character starting pointcan be improved by integrating the magnetic signal and then looking atthe locations where the integrated signal goes to zero. The signalpicked up by the magnetic head is the derivative of the magnetic inkpassing across the head. Integrating this signal yields the amount ofink under the head at any point in time. When this integrated signalgoes to zero, we know we have no ink under the head and are at a blankspace between characters. The midpoint of a blank space identified bythe integral can be used as the starting point of next character to beread. This method can also be used to locate characters in the timedomain algorithm.

Cepstrum is the Fourier transform of a spectrum. It is thus the spectrumof a spectrum, and has certain properties that make it useful in manytypes of signal analysis. One of its more powerful attributes is thefact that any periodicities, or repeated patterns, in a spectrum will besensed as one or two specific components in the cepstrum. If a spectrumcontains several sets of sidebands or harmonic series, they can beconfusing because of overlap. But in the cepstrum, they will beseparated in a way similar to the way the spectrum separates repetitivetime patterns in the waveform. False responses to such things asgearboxes and rolling element bearing vibrations may be avoided bycepstrum analysis.

Thus, an additional power spectrum (Cepstrum) calculation may furtherenhance identification of the character. When matching against atemplate in the frequency domain some characters are more likely thanothers to give an incorrect match because spectral lines are in similarlocations. For those characters, an additional processing step is to beperformed to further discriminate between close character. A cepstrum isperformed and a cepstrum template used for the match.

After characters have been assigned to each character position usingboth optical and magnetic analysis the data is accessed and usedtogether to obtain a final selected character. FIG. 4 is a flow diagramrepresenting the analysis of magnetic and optical data to assign acharacter to each character position, if possible. In FIG. 4 the opticalanalysis begins at step 45 where the optical data is read from thecheck. A step 47 is then performed to normalize the data and thenormalized data is compared with the character templates in step 49 todetermine a character (or characters) for each character position instep 51. A confidence value is also determined in step 51 and for eachcharacter position an identified character and its confidence value isstored in step 53.

The analysis of the magnetic data begins at step 57 in which themagnetic character data is read from a check. In a step 59 the magneticdata is normalized which, as discussed above, can be both in amplitudeand position of detected peaks. The normalized data is then compared instep 61 to character templates in a step 61 to identify a character foreach character position and in step 63 a certainty value is identifiedfor each identified character. After determining the certainty factor, arepresentation of each character identified for each character positionis stored along with a certainty parameter.

At the completion of step 65, a step 67 is performed in which acharacter is assigned to each position based on the magnetic charactersand confidence levels stored in step 65 and the optical characters andconfidence levels stored in step 53. Character assignment in step 67 isperformed with a set of rules described immediately below. When both themagnetic and the optical characters are the same and confidence for bothis high, assign the selected characters. When the magnetic character hashigh confidence and the optical character for the same characterposition has low confidence, assign the magnetic character. When themagnetic confidence is low and the optical character has a highconfidence parameter, assign the optical character to the characterposition. When both the optical character and the magnetic characterindicate a low confidence, fail the analysis and re-read the check orresort to other analysis operations such as visual analysis. Finallywhen both characters have high confidence parameters but identify adifferent character, fail the analysis.

The preceding embodiments involve the separate analysis of optical scandata and magnetic scan data and the combination of the results of theanalysis. An additional analysis tool is discussed below in which theoptical scan data and magnetic scan data are combined before analysis ofthe scanned data. Such a tool may be used independently of the priorseparate analysis embodiment or it may be used in conjunction withseparate analysis. For example, when separate analysis does not providesufficiently certain character assignment the joint analysis tool may beemployed to provide additional certainty for all or selected characterpositions. Also for example, the analysis of combined data may beemployed in place of all separate analysis. It is presently envisionedthat a combination of joint and separate analysis provides the bestresults.

The specification of the MICR font defines the magnetic characteristicsof a fourteen character set including the numbers zero through nineinclusive and four specialty characters. Each character is defined bythe response of a specified magnetic measurement circuit and theposition and amplitude of positive and negative peaks. There are sevenmeasurement regions which define each character. Since there arefourteen characters defined by these measurements, this is generallyreferred to as an under determined system. As an under determinedsystem, mathematically, this means that characters can be formed bylinear combinations of other characters, hence, they are not orthogonalnor are they completely unique.

The Optical Characteristics of an MICR characters are significantly lessstrict than the magnetic characters in the specification. They arespecified by a seven (7) wide by nine (9) tall grid. While the systemhas sixty-three unique measurement points which define an overdetermined system allowing the implementation of high end signalprocessing techniques, the ease with which the optical data can becorrupted by normal actions makes implementation difficult andpotentially error prone.

Prior recognition systems have been limited to techniques such as peakcounting and placement or envelope matching. These systems work to areasonable level but misreads or non reads may occur as print qualityvaries. In particular, the recent acceptance of laser printed documentshas created significant challenges since the dimensions of the MICR fontcharacters do not fix integrally into the print capability of the laserprinter. As such, characters often fail to meet the specifieddimensions. Several of the characters have heuristically similarwaveforms with the only differentiator being width of a zone of nosignal.

Print quality, which, when poor, may cause magnetically read waveformsto be smeared or have peaks in the wrong places. This may cause amisread on many check readers relying simply on magnetic signatures.Envelope evaluation systems will contain large amounts of energy outsidethe envelope which will cause high energy counts. As such they will failto recognize a character. Or possibly, the peaks may be shifted to a newregion which would cause a misread.

Magnetic systems are also subject to Electromagnetic Interference (EMI)commonly produced by monitors and merchant theft detection devices whichare frequently located next to a MICR reading device. This causes extrapeaks to appear or possibly even washes out existing peaks on somecharacters which have small strokes. This could again cause peaks in thewrong places and energy outside the envelope window. A second set ofissues involves mechanical transport of the documents which can producemisreads due to waveform corruption (elongation of characters,shortening of characters etc.)

At the point of sale and bank environment, an interesting problem occurswhen a check holder folds the document prior to submission. When thecrease falls within a character stroke, artificial spikes can becreated, potentially causing problems with a magnetic recognitionsystem.

The application of Optical Character Recognition techniques introducessome practical problems as well. While mechanical variations are lessproblematic there are frequently issues with corruption of the opticaldata caused by normal usage, such as crossover from the signature fieldinto the MICR line by tailed characters such as “j”, “y”, “g” etc. Suchcrossovers produce strokes in the optical field which can confuse arecognition system. Similarly, smears of the MICR ink may have a greaterimpact on the optical character than the magnetic or even just dirt fromcarrying in one's wallet or pant pocket can cause problems with Opticalreads. Less important but still problematic for some algorithms is thebackground which is present on a check which can cause confusion as towhat is the character and what is background information.

Advantages may be achieved over the systems discussed above whichseparately analyze optical and magnetic data and neglects the fact thatsimultaneous measurement of the same data with multiple sensors ignorespossibly critical information which can be used across the system. Withthe present embodiment multispectral signal processing is proposed togain a recognition advantage. A measurement error typically only impactsone spectra of data. An extra stroke created by a flagged character willbe visible in the optical data but not the magnetic data. A magneticcharacter whose signature has been changed due to a mid character foldwill not have the same impact on the optical signature.

FIG. 5 is a general flow diagram representing multispectral analysis.Initially, a check is scanned as discussed with regard to FIG. 2 toproduce optical character data and magnetic character data representingthe scanned MICR character set. Magnetic scanning is represented atblock 71 and optical scanning is represented at block 77 of FIG. 5. Itshould be mentioned that the character scanning represented in blocks 71and 77 may already have been performed and optical character data andmagnetic character data may already be stored in the control 25 andmemory 27. The magnetic character data is read from storage in block 73and normalized in block 75 to a standard for combination with opticalcharacter data which is read from storage in block 79 and alsonormalized for combination in block 81.

The normalized optical data (block 81) and the normalized magnetic data(block 75) are then provided to a combining step 83. The combining stepaligns the data based on character start positions and forms a vectorrepresentation of the combined data. An analysis step 85 is thenperformed, the results of which are applied to a character assignmentstep 87. In cases where the multispectral analysis is the only orcontrolling analysis, flow proceeds to block 89 where the charactersassigned in block 87 are used. Alternatively, in cases where thecharacter assignments of block 87 are to be used in conjunction withother types of analysis an optional procedure represented in dashed box91 is performed. In the optional procedure, the character valuesassigned in block 87 are compared in block 93 with other possibleassigned values, such as from separate optical and magnetic analysis,and a character value decision is made at block 95.

In the analysis block 85 optional types of data evaluation of thecombined data may be performed. For example, prior to analysis, datarepresenting combined optical and magnetic scans of the standardtemplates may be created as a combined template. The combined scanneddata may then be analyzed in block 85 by comparison with the combinedtemplate data. Such comparisons may comprise matrix multiplication andselection as described above with regard to FIG. 2. Alternatively, erroranalysis may be performed. With error analysis, the combined data vectoris analyzed for error when compared to the combined template.

In the present scanning technique there are a finite set of acceptabledata patterns for the character set, fourteen (14) to be precise. Eachcharacter has a known desired combined template signal. The followingdiscussion is independent of the MICR font recognition problem,extracted from generalized statistical theory. First, establish

d=wm+e

where m represents the measured data, w represents a filter operation, drepresents the desired character waveform and e represents an errorcomponent associated with the measurement (all bold characters representvectors of data). By making the usual assumptions on the errorcomponent, that it is white guassian noise, we state that theexpectation value of the error of a period of time is 0.

E[e(n)]=0 with an autocorrelation function δ(n)=σ̂2 for i=0 only,otherwise the autocorrelation function=0.

The Error Estimate is then:

$\begin{matrix}{{\xi \; {est}} =  {\sum{{{d_{est}(n)}}\bigwedge 2}} |} \\{= {\sum{\sum{\sum{w_{i}w_{k}^{*}\mspace{14mu} {m( {n - k} )}\mspace{20mu} {m^{*}( {n - t} )}}}}}}\end{matrix}\quad$

The last two terms, m and m* relate to the autocorrelation matrix Φ ofthe measured data so the error reduces to

ξest=w ^(H) Φw

Given the estimate of the error for each character, the minimum error isthen:

$\begin{matrix}{{\xi \; \min} = {{\xi \; d} - {\xi \; {est}}}} \\{= {{\xi \; d} - {w^{H}\; \Phi \; w}}}\end{matrix}\quad$

Further, the ξest can be recast in terms of strictly the desired dataand the measured data removing the requirement to actually calculate thefilter itself:

ξ min=d ^(H) d−d ^(H) A(A ^(H) A)⁻¹ A ^(H) d

where A is defined as a data matrix (a vector) and recall that d issimply the vector representing the character we desire to test to see ifit can produce a minimum error result. All this is independent of theproblem of selecting characters and is a general and well documentedmathematical conclusion (ref:Adaptive Filter Theory, Simon Haykin andothers).

Since we are merging the data of the over determined opticalmeasurements with the under determined magnetic measurements, a systemexist that is, by definition over determined meaning each character isorthogonal to each other character. As such, only one character canprovide a minimum error estimate ξ min and hence, the selected characteris the character with the minimum error. As a final check the filter wis calculated which should yield the desired waveform for thevalidation. By utilization this approach, any improperly placed markswill be weighted as an error.

Eigenvector decomposition may also be used as an analysis tool in block85. As mentioned above, an autocorrelation matrix, Φ, can be generatedfrom for the combined template data set which contains all the knowncharacters in the font set. This matrix can be subjected to anEigenvalue Decomposition which will produce a set of eigenvalues (λi)and eigenvectors (ei). The eigenvectors, by definition, produce a set oforthogonal vectors, each one representing a filter for the uniqueattributes of the signals in question. As such, they produce a series offilters which, when applied to a signal, produce a set of subspaces,each one containing only data that is coherent with the subspaceassociated with the eigenvalue.

The analysis of the combined scanned data may employ eigenvectordecomposition as a preprocessor which allows operating the filtersassociated with each character on the character waveform. The resultwould be a set of signal subspaces, one subspace per character alongwith a noise subspace which captures anything which is not part of acharacter (background for example). This would leave processing of thesignal subspaces for recognition. This preprocessing in some casesreduces the opportunity for interference to impact recognition.

While the invention herein disclosed has been described by means ofspecific embodiments and applications thereof, other modifications,variations, and arrangements of the present invention may be made inaccordance with the above teachings other than as specifically describedto practice the invention within the spirit and scope defined by thefollowing claims.

1. A method of evaluating a set of printed MICR characters, comprising:providing a template data file representing values of standard templateMICR characters; scanning the printed set of MICR characters andaccumulating character data representing the scanned characters; firstevaluating the character data against the template data file using acoarse analysis method to identify the scanned characters and toidentify scanned character characteristics; second evaluating thecharacter data and the scanned character characteristics identified inthe first evaluating step against the template data file using a strictanalysis method; and identifying characters in response to the secondevaluating step.
 2. The method of claim 1 wherein the coarse analysismethod of the first evaluating step comprises first normalizing thecharacter data to a first normalized value.
 3. The method of claim 2wherein the first normalized value is determined from peak values of theaccumulated character data.
 4. The method of claim 3 wherein the strictanalysis method of the second evaluating step comprises normalizing thecharacter data to a second normalized value determined from peak valuesof the template data file.
 5. The method of claim 4 wherein the secondnormalized value clips peak values of the accumulated character data topeak values of the template data.
 6. The method of claim 1 wherein thefirst and second evaluating steps comprise matrix multiplication of thetemplate character data and the accumulated character data and thestrict analysis method of the second evaluating step further comprisessubtracting from the matrix multiplication a value representingdifferences between the template character data and the accumulatedcharacter data.
 7. The method of claim 6 wherein the strict analysismethod of the second evaluating step comprises subtracting from thematrix multiplication an amount representing a value of the accumulatedcharacter data when the template character data has a value of zero. 8.The method of claim 7 wherein the strict analysis method of the secondevaluating step comprises subtracting from the matrix multiplication anamount representing a value of the template data when the character datahas a value of zero.
 9. A method of evaluating a set of printed MICRcharacters, comprising: providing a template data file representing thefrequency spectrums of a set of template MICR characters; magneticallyscanning the set of printed MICR characters and accumulating magneticcharacter data; generating scanned character frequency spectrum datarepresenting portions of the accumulated magnetic character data;comparing the scanned character frequency spectrum data to the templatedata; and assigning character values to the printed MICR characters inresponse to the comparing step.
 10. The method of claim 9 comprisingnormalizing the accumulated magnetic character value data.
 11. Themethod of claim 9 wherein the step of generating includes converting theaccumulated magnetic character data to the frequency domain.
 12. Themethod of claim 10 comprising normalizing the data converted to thefrequency domain.
 13. The method of claim 12 wherein the normalizingcomprises normalizing a signal power of the data converted to thefrequency domain.
 14. The method of claim 11 wherein the converting stepcomprises performing a fast fourier transform of the accumulatedmagnetic character data.
 15. The method of claim 11 wherein theconverting step comprises determining a magnitude of individualfrequency domain values.
 16. The method of claim 15 wherein the step ofdetermining the magnitude comprises calculating a square root of the sumof the square of real and imaginary components of the converted magneticcharacter data.
 17. The method of claim 9 wherein the comparing stepcomprises comparing a frequency spectrum power determined from thetemplate data file with a frequency spectrum power determined in thegenerating step.
 18. The method of claim 9 wherein the generating stepcomprises generating a cepstrum from the frequency spectrum of theaccumulated data.
 19. The method of claim 9 comprising filtering highfrequency components from the scanned character frequency spectrum data.20. The method of identifying character positions in a set of printedMICR characters, comprising: magnetically scanning a set of printed MICRcharacters; accumulating time based magnetic character data representingthe scanned MICR characters; integrating the magnetic character data andidentifying when the integral is zero; identifying from the integratingstep a beginning point for a printed character.
 21. The method of claim20 wherein the identifying step includes recording a blank space ofmagnetic character data during which the integral of the magneticcharacter is zero and identifying the mid point of the blank space.